Journal
BIOINFORMATICS
Volume 26, Issue 17, Pages 2183-2189Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq354
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Funding
- National Institute of General Medicine [T32GM074934]
- National Cancer Institute [R03CA137805]
- National Institute of Dental and Craniofacial Research [K25DE016863, P20RR017696]
- National Science Foundation Division of Mathematical Sciences [0604666]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0604666] Funding Source: National Science Foundation
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Motivation: Highly sensitive and specific screening tools may reduce disease -related mortality by enabling physicians to diagnose diseases in asymptomatic patients or at-risk individuals. Diagnostic tests based on multiple biomarkers may achieve the needed sensitivity and specificity to realize this clinical gain. Results: Logic regression, a multivariable regression method predicting an outcome using logical combinations of binary predictors, yields interpretable models of the complex interactions in biologic systems. However, its performance degrades in noisy data. We extend logic regression for classification to an ensemble of logic trees (Logic Forest, LF). We conduct simulation studies comparing the ability of logic regression and LF to identify variable interactions predictive of disease status. Our findings indicate LF is superior to logic regression for identifying important predictors. We apply our method to single nucleotide polymorphism data to determine associations of genetic and health factors with periodontal disease.
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